在放射技术领域使用词嵌入的日本同义词识别的准确性比较
Ayako Yagahara1, Noriya Yokohama2
1Faculty of Health Sciences, Hokkaido University of Science, 7-Jo 15-4-1 Maeda, Teine, Sapporo, Hokkaido, 006-8585, Japan. yagahara-a@hus.ac.jp.
Scientific reports
|December 16, 2023
概括
这项研究评估了Word2vec和fastText,以检测日本放射技术术语中的同义词. fastText与CBOW在识别共享n-grams的同义词方面表现出色,这对这个领域至关重要.
科学领域:
- 医疗成像医学成像
- 辐射技术 辐射技术
- 自然语言处理自然语言处理.
背景情况:
- 精确的术语对于放射技术至关重要,它涵盖了辐射和医学成像.
- 自动同义词检测需要考虑特定的语言特征,如日语.
- 现有的方法可能无法完全捕捉到专业科学领域的细微差别.
研究的目的:
- 为了评估Word2vec和fastText在日本放射技术术语中的自动同义词检测的准确性.
- 为了优化性能,比较不同的模型架构 (CBOW,skip-gram) 和矢量大小.
- 确定在这个专业领域中制定术语的最有效的模型.
主要方法:
- 收集了大约34万个日本在放射技术上的摘要.
- 预处理了抽象数据并训练了具有不同架构和矢量大小的Word2vec和fastText模型.
- 用专家输入生成基线同义词集,并使用精度,回忆,F1得分和准确度比较模型生成的同义词.
主要成果:
- 使用连续词袋 (CBOW) 和300个维度的fastText模型在同义词检测中展示了最高的精度,特别是在共享n-grams的术语中.
- 使用skip-gram和Word2vec的fastText对于缺乏常见n-gram的同义词更有效.
- fastText CBOW被证明是放射技术的优势,因为普遍存在n-grams,而Word2vec CBOW更适合使用缩写的信息学.
结论:
- fastText与CBOW在日本放射技术术语中对于同义词检测非常有效,优于其他测试模型.
- 模型选择应考虑特定科学领域的语言特征,例如n-gram流行率.
- 这项研究有助于改善术语管理和自然语言处理在医学成像中的应用.
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